Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts, such as grid computing and massively parallel supercomputers. The Handbook of Parallel Computing and Statistics systematically applies the principles of parallel computing for solving increasingly complex problems in statistics research. This unique reference weaves together the principles and theoretical models of parallel computing with the design, analysis, and application of algorithms for solving statistical problems. After a brief introduction to parallel computing, the book explores the architecture, programming, and computational aspects of parallel processing. Focus then turns to optimization methods followed by statistical applications. These applications include algorithms for predictive modeling, adaptive design, real-time estimation of higher-order moments and cumulants, data mining, econometrics, and Bayesian computation. Expert contributors summarize recent results and explore new directions in these areas. Its intricate combination of theory and practical applications makes the Handbook of Parallel Computing and Statistics an ideal companion for helping solve the abundance of computation-intensive statistical problems arising in a variety of fields.

This volume features original contributions and invited review articles on mathematical statistics, statistical simulation and experimental design. The selected peer-reviewed contributions originate from the 8th International Workshop on Simulation held in Vienna in 2015. The book is intended for mathematical statisticians, Ph.D. students and statisticians working in medicine, engineering, pharmacy, psychology, agriculture and other related fields. The International Workshops on Simulation are devoted to statistical techniques in stochastic simulation, data collection, design of scientific experiments and studies representing broad areas of interest. The first 6 workshops took place in St. Petersburg, Russia, in 1994 - 2009 and the 7th workshop was held in Rimini, Italy, in 2013.

This workbook and solutions manual is intended for advanced undergraduate or beginning graduate students as a supplement to a traditional course in numerical mathematics and as preparation for independent research involving numerical mathematics. The solutions manual provides complete MATLAB code and numerical results for each of the exercises in the workbook and will be especially useful for those students without previous MATLAB programming experience. It is also valuable for classroom instructors to help pinpoint the author's intent in each exercise and to provide a model for graders. Upon completion of this material, students will have a working knowledge of MATLAB programming, they will have themselves programmed algorithms encountered in classwork and textbooks, and they will know how to check and verify their own programs against hand calculations and by reference to theoretical results, special polynomial solutions and other specialized solutions. No previous programming experience with MATLAB is necessary.

In social science outstanding results are yielded by advanced simulation methods, based on state of the art software technologies and an appropriate combination of qualitative and quantitative methods. This book presents examples of successful applications of modelling and computing in social science: business and logistic process simulation and optimization, deeper knowledge extractions from big data, better understanding and predicting of social behaviour and modelling health and environment changes.

The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.

This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques.

Financial, Macro and Micro Econometrics Using R, Volume 42, provides state-of-the-art information on important topics in econometrics, including multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, financial market jumps and co-jumps, among other topics.

Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets. Fundamentals of Interactive Statistical GraphicsThe first part of the book summarizes principles and methodology, demonstrating how the different graphical representations of variables of a dataset are effectively used in an interactive setting. The authors introduce the most important plots and their interactive controls. They also examine various types of data, relations between variables, and plot ensembles. Case Studies Illustrate the PrinciplesThe second section focuses on nine case studies. Each case study describes the background, lists the main goals of the analysis and the variables in the dataset, shows what further numerical procedures can add to the graphical analysis, and summarizes important findings. Wherever applicable, the authors also provide the numerical analysis for datasets found in Cox and Snell's landmark book. Understand How to Analyze Data through Graphical Means This full-color text shows that interactive graphical methods complement the traditional statistical toolbox to achieve more complete, easier to understand, and easier to interpret analyses.

This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome obstacles that could not be previously solved by methods such as regression and time series modelling in the areas of economics, meteorology, and transportation.

"If mathematical modeling is the process of turning real phenomena into mathematical abstractions, then numerical computation is largely about the transformation from abstract mathematics to concrete reality. Many science and engineering disciplines have long benefited from the tremendous value of the correspondence between quantitative information and mathematical manipulation." -from the Preface Fundamentals of Numerical Computation is an advanced undergraduate-level introduction to the mathematics and use of algorithms for the fundamental problems of numerical computation: linear algebra, finding roots, approximating data and functions, and solving differential equations. The book is organized with simpler methods in the first half and more advanced methods in the second half, allowing use for either a single course or a sequence of two courses. The authors take readers from basic to advanced methods, illustrating them with over 200 self-contained MATLAB functions and examples designed for those with no prior MATLAB experience. Although the text provides many examples, exercises, and illustrations, the aim of the authors is not to provide a cookbook per se, but rather an exploration of the principles of cooking. Professors Driscoll and Braun have developed an online resource that includes well-tested materials related to every chapter. Among these materials are lecture-related slides and videos, ideas for student projects, laboratory exercises, computational examples and scripts, and all the functions presented in the book.

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs. The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression, L1 and q-quantile regression, regression in a spatial domain, ridge regression, semiparametric regression, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling readers to promptly perform their own regression runs. A Comprehensive, Accessible Source on Regression Methodology and ModelingRequiring only basic knowledge of statistics and calculus, this book discusses how to use regression analysis for decision making and problem solving. It shows readers the power and diversity of regression techniques without overwhelming them with calculations.

Thoroughly revised and updated, The Art of Modeling in Science and Engineering with Mathematica (R), Second Edition explores the mathematical tools and procedures used in modeling based on the laws of conservation of mass, energy, momentum, and electrical charge. The authors have culled and consolidated the best from the first edition and expanded the range of applied examples to reach a wider audience. The text proceeds, in measured steps, from simple models of real-world problems at the algebraic and ordinary differential equations (ODE) levels to more sophisticated models requiring partial differential equations. The traditional solution methods are supplemented with Mathematica , which is used throughout the text to arrive at solutions for many of the problems presented. The text is enlivened with a host of illustrations and practice problems drawn from classical and contemporary sources. They range from Thomson's famous experiment to determine e/m and Euler's model for the buckling of a strut to an analysis of the propagation of emissions and the performance of wind turbines. The mathematical tools required are first explained in separate chapters and then carried along throughout the text to solve and analyze the models. Commentaries at the end of each illustration draw attention to the pitfalls to be avoided and, perhaps most important, alert the reader to unexpected results that defy conventional wisdom. These features and more make the book the perfect tool for resolving three common difficulties: the proper choice of model, the absence of precise solutions, and the need to make suitable simplifying assumptions and approximations. The book covers a wide range of physical processes and phenomena drawn from various disciplines and clearly illuminates the link between the physical system being modeled and the mathematical expression that results.

The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called "FuzzySTs" which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.

What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https: //www.functionalaestheticsbook.com

Without question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students' anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses. Key features of the text: Step-by-step instruction and screenshots Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter Call-out boxes provided, highlighting important information as appropriate SPSS output explained, with written results provided using the popular, widely recognized APA format End-of-chapter exercises included, allowing for additional practice SPSS datasets available on the publisher's website New to the Fourth Edition: Fully updated to SPSS 28 Updated screenshots in full color to reflect changes in SPSS software system (version 28) Exercises updated with up-to-date examples Exact p-values provided (consist with APA recommendations)

Most transformations and large-scale change programs fail, but in a rapidly changing world change is becoming more and more critical for survival. The HERO Transformation Playbook is your step-by-step playbook of EXACTLY how to deliver successful transformations and large-scale change programs with the best chance of success using the HERO Transformation Framework: a clear method to help you design transformation for maximum enterprise value creation and then deliver the outcome in a repeatable fashion. We built our framework through trial and error, learning from our mistakes and successes and solving common issues we came across and pitfalls that we have seen time and again. We then spent many years honing the framework, removing the fluff, distilling the concepts until it contained everything you need to succeed in the challenging world of change. In this book we teach you everything we've learned - including all of the roles, processes, meetings, governance, and templates for you to follow and apply to your transformation today - so that you can crack the code of change and lead successful transformations on your own. The more successful transformations that are delivered, the better the world will be for everyone!

The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to the 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Universite Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original research in Bayesian computation, application, and theory.

Compositional Data Analysis in Practice is a user-oriented practical guide to the analysis of data with the property of a constant sum, for example percentages adding up to 100%. Compositional data can give misleading results if regular statistical methods are applied, and are best analysed by first transforming them to logarithms of ratios. This book explains how this transformation affects the analysis, results and interpretation of this very special type of data. All aspects of compositional data analysis are considered: visualization, modelling, dimension-reduction, clustering and variable selection, with many examples in the fields of food science, archaeology, sociology and biochemistry, and a final chapter containing a complete case study using fatty acid compositions in ecology. The applicability of these methods extends to other fields such as linguistics, geochemistry, marketing, economics and finance. R Software The following repository contains data files and R scripts from the book https://github.com/michaelgreenacre/CODAinPractice. The R package easyCODA, which accompanies this book, is available on CRAN -- note that you should have version 0.25 or higher. The latest version of the package will always be available on R-Forge and can be installed from R with this instruction: install.packages("easyCODA", repos="http://R-Forge.R-project.org").

* Targests readers with a background in programming, interested in an introduction/refresher in statistical hypothesis testing * Uses Python throughout * Provides the reader with the opportunity of using the book whenever needed rather than following a sequential path.

A "how to" guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs. Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies, Analysis of Biomarker Data: A Practical Guide also features: A greater emphasis on the application of methods as opposed to the underlying statistical and mathematical theory The use of SAS(R), R, and other software throughout to illustrate the presented calculations for each example Numerous exercises based on real-world data as well as solutions to the problems to aid in reader comprehension The principles of good research study design and the methods for assessing the quality of a newly proposed biomarker A companion website that includes a software appendix with multiple types of software and complete data sets from the book's examples Analysis of Biomarker Data: A Practical Guide is an ideal upper-undergraduate and graduate-level textbook for courses in the biological or environmental sciences. An excellent reference for statisticians who routinely analyze and interpret biomarker data, the book is also useful for researchers who wish to perform their own analyses of biomarker data, such as toxicologists, pharmacologists, epidemiologists, environmental and clinical laboratory scientists, and other professionals in the health and environmental sciences.

This is the first book of its kind which teaches matrix algebra, allowing the student to learn the material by actually working with matrix objects in modern computer environment of R. Instead of a calculator, R is a vastly more powerful free software and graphics system.

The book provides a comprehensive overview of matrix theory without being bogged down in proofs or tedium. The reader can check each matrix result with numerical examples of exactly what they mean and understand their implications. The book does not shy away from advanced topics, especially the ones with practical applications.

Enterprise Level Security 2: Advanced Topics in an Uncertain World follows on from the authors' first book on Enterprise Level Security (ELS), which covered the basic concepts of ELS and the discoveries made during the first eight years of its development. This book follows on from this to give a discussion of advanced topics and solutions, derived from 16 years of research, pilots, and operational trials in putting an enterprise system together. The chapters cover specific advanced topics derived from painful mistakes and numerous revisions of processes. This book covers many of the topics omitted from the first book including multi-factor authentication, cloud key management, enterprise change management, entity veracity, homomorphic computing, device management, mobile ad hoc, big data, mediation, and several other topics. The ELS model of enterprise security is endorsed by the Secretary of the Air Force for Air Force computing systems and is a candidate for DoD systems under the Joint Information Environment Program. The book is intended for enterprise IT architecture developers, application developers, and IT security professionals. This is a unique approach to end-to-end security and fills a niche in the market.

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows. New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book's CRC Press web page.

With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors' website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.